* Add implicit feedback methods to package init file.

Author: gasevi

Diff for: .gitignore

@@ -4,7 +4,7 @@ cmake_install.cmake
 Makefile
 build/
 dist/
-pypackage/
+pypackage/setup.py
 pyreclab.egg-info/
 pyreclab/
 install_manifest.txt

Diff for: examples/testIFAls.py

@@ -3,7 +3,7 @@
 
 if __name__ == '__main__':
 
-   ifals = pyreclab.IFAls( dataset = 'lastfm-dataset.txt',
+   ifals = pyreclab.IFAls( dataset = 'dataset/u1.base',
                            dlmchar = b'\t',
                            header = False,
                            usercol = 0,
@@ -16,8 +16,8 @@
    end = time.clock()
    print( 'training time: ' + str( end - start ) )
 
-   ranking = ifals.recommend( 'user_000436', 5, includeRated = False )
-   print( 'recommendation for user user_000436: ' + str( ranking ) )
+   ranking = ifals.recommend( '457', 5, includeRated = False )
+   print( 'recommendation for user 457: ' + str( ranking ) )
 
    print( '-> recommendation test' )
    start = time.clock()

Diff for: examples/testIFAlsCg.py

@@ -3,7 +3,7 @@
 
 if __name__ == '__main__':
 
-   ifalscg = pyreclab.IFAlsConjugateGradient( dataset = 'dataset/lastfm-dataset.txt',
+   ifalscg = pyreclab.IFAlsConjugateGradient( dataset = 'dataset/u1.base',
                                               dlmchar = b'\t',
                                               header = False,
                                               usercol = 0,
@@ -16,8 +16,8 @@
    end = time.clock()
    print( 'training time: ' + str( end - start ) )
 
-   ranking = ifalscg.recommend( 'user_000436', 5, includeRated = False )
-   print( 'recommendation for user user_000436: ' + str( ranking ) )
+   ranking = ifalscg.recommend( '457', 5, includeRated = False )
+   print( 'recommendation for user 457: ' + str( ranking ) )
 
    print( '-> recommendation test' )
    start = time.clock()

Diff for: pypackage/__init__.py

@@ -1,10 +1,12 @@
-from pyreclab.libpyreclab import MostPopular, UserAvg, ItemAvg, SlopeOne, UserKnn, ItemKnn, SVD
+from pyreclab.libpyreclab import MostPopular, UserAvg, ItemAvg, SlopeOne, UserKnn, ItemKnn, SVD, IFAls, IFAlsConjugateGradient
 
 __all__ = [ 'MostPopular',
             'UserAvg',
             'ItemAvg',
             'SlopeOne',
             'UserKnn',
             'ItemKnn',
-            'SVD' ]
+            'SVD',
+            'IFAls',
+            'IFAlsConjugateGradient' ]